IBM Z Platform for Apache Spark Modernization Guide

IBM Z Platform for Apache Spark enables running Spark applications on z/OS, leveraging the platform's strengths in data processing and security. It allows users to process data where it resides, reducing data movement.

It integrates with existing z/OS security and resource management features, such as RACF for authentication and authorization, and WLM for workload management. This ensures Spark workloads are managed within the z/OS environment.

The platform supports standard Spark APIs, allowing developers to use familiar programming languages such as Scala, Java, and Python. It also supports Spark SQL for querying structured data.

Common operations include submitting Spark applications using `spark-submit`, querying data with Spark SQL, and configuring Spark properties via `spark-defaults.conf`. The `jps` command can be used to check running Java processes, including Spark.

The platform exposes standard Spark APIs, accessible via REST endpoints for job submission and monitoring. Integration is supported through Scala, Java, and Python SDKs. Communication occurs over TCP/IP.

Key components include the Spark Master, Worker nodes, and the Spark History Server. These communicate using standard Spark protocols. Data is typically stored in z/OS datasets, VSAM files, or DB2.

By running Spark on z/OS, organizations can leverage existing infrastructure and skills, reducing the need to move data off the platform. This improves performance and reduces costs associated with data transfer and storage.

It enables real-time analytics on z/OS data, allowing businesses to gain insights from their mainframe data without the latency of moving data to other platforms. This supports faster decision-making and improved business outcomes.

Authentication methods include RACF, LDAP, and Kerberos. The access control model is based on RACF roles and permissions, providing granular control over access to Spark resources and data.

Data encryption is supported both in transit and at rest, using standard encryption algorithms. Audit logging captures Spark events and user activities, providing a comprehensive audit trail for security and compliance purposes.

Administration is performed through a combination of z/OS console commands, the Spark web UI, and configuration files. User management is handled through RACF, leveraging existing z/OS security infrastructure.

Monitoring and logging capabilities include the Spark History Server for tracking application execution, z/OS SMF records for system-level monitoring, and standard Spark logging for debugging and troubleshooting.